Notebook
CS
2/9/2021
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library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## report mean ki per ROI
dataMNI <- read.csv('~/Dropbox/DCCN/Creativity/roi_results_mni.csv')
dataMNI %>%
group_by(ROI) %>%
dplyr::summarise(mean = mean(meanRoiContrastEstimate),
sd = sd(meanRoiContrastEstimate)) library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.0.4 ✓ stringr 1.4.0
## ✓ tidyr 1.1.2 ✓ forcats 0.5.0
## ✓ readr 1.4.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## graph the distribution in box plot
ggplot(dataMNI, aes(x=ROI, y=meanRoiContrastEstimate)) +
geom_boxplot() +
coord_flip()
## look at the density plot
ggplot(dataMNI, aes(x=meanRoiContrastEstimate, group=ROI)) +
geom_line(stat="density")
## report mean ki per ROI
dataNative <- read.csv('~/Dropbox/DCCN/Creativity/roi_results_native.csv')
dataNative %>%
group_by(ROI) %>%
dplyr::summarise(mean = mean(meanRoiContrastEstimate),
sd = sd(meanRoiContrastEstimate)) ## graph the distribution in box plot
ggplot(dataNative, aes(x=ROI, y=meanRoiContrastEstimate)) +
geom_boxplot() +
coord_flip()
## look at the density plot
ggplot(dataNative, aes(x=meanRoiContrastEstimate, group=ROI)) +
geom_line(stat="density")
## Ruben shared that we are only using native space and only whole caudate, putamen and VS
library(sjPlot)
dataAll <- read.csv('~/Dropbox/DCCN/Creativity/CreativityIDsheet_MissingDataRemoved.csv')
# turn drug conditions into factor levels
dataAll$Drug <- factor(dataAll$Drug, levels = c("MPH","SUL","PBO"));
# set contrasts to sum-to-zero
options(contrasts=c("contr.sum", "contr.poly"))
# set two dataframes for the contrast between MPH and PBO - between SUL and PBO
df_MPH <- dplyr::filter(dataAll, Drug %in% c("MPH","PBO"))
df_SUL <- dplyr::filter(dataAll, Drug %in% c("SUL","PBO"))SelectData <- dataNative[grepl("wholeCaudate", dataNative[["ROI"]]) | grepl("wholePutamen", dataNative[["ROI"]]) | grepl("VS", dataNative[["ROI"]]), ]
SelectData <- SelectData[-grep("_", SelectData$ROI),]
ggplot(SelectData, aes(x=ROI, y=meanRoiContrastEstimate)) +
geom_boxplot() +
coord_flip()
ggplot(SelectData, aes(x=meanRoiContrastEstimate, group=ROI)) +
geom_line(stat="density")
# How do different ROI Ki s relate to each other?
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(dataAll[,2:4], pch = 19, cex = 0.5,
lower.panel=NULL)
library(lme4)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack# Does session number have an effect on convergent thinking?
ggplot(dataAll, aes(x=as.factor(Session), y=Convergent_Pasta)) +
geom_boxplot(fill="slateblue", alpha=0.2) +
xlab("Session")
# Total number of generated ideas
SessionEffectTotal <- lmer(Total ~ 1 + Session + (1 + Session|| ID), data = dataAll, REML=F)
print(summary(SessionEffectTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Total ~ 1 + Session + ((1 | ID) + (0 + Session | ID))
## Data: dataAll
##
## AIC BIC logLik deviance df.resid
## 2202.0 2220.2 -1096.0 2192.0 277
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0605 -0.4235 -0.0551 0.3522 4.1868
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 167.328 12.936
## ID.1 Session 9.404 3.067
## Residual 56.000 7.483
## Number of obs: 282, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 28.0887 1.7805 15.776
## Session 2.3457 0.6308 3.719plot_model(SessionEffectTotal, type = "pred", show.data=TRUE, terms = c("Session"))
# Pure convergent scores
SessionEffect <- lmer(Convergent_Pasta ~ 1 + Session + (1 + Session|| ID), data = dataAll, REML=F)
print(summary(SessionEffect), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Session + ((1 | ID) + (0 + Session | ID))
## Data: dataAll
##
## AIC BIC logLik deviance df.resid
## 2183.2 2201.4 -1086.6 2173.2 277
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1205 -0.4193 -0.0678 0.3707 5.0262
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 144.02 12.001
## ID.1 Session 9.27 3.045
## Residual 53.67 7.326
## Number of obs: 282, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 16.9078 1.6924 9.990
## Session 2.8777 0.6198 4.643plot_model(SessionEffect, type = "pred", show.data=TRUE, terms = c("Session"))
# convergent/divergent ratio
SessionEffectRatio <- lmer(Con_Div ~ 1 + Session + (1 + Session|| ID), data = dataAll, REML=F)
print(summary(SessionEffectRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Session + ((1 | ID) + (0 + Session | ID))
## Data: dataAll
##
## AIC BIC logLik deviance df.resid
## 1796.2 1814.1 -893.1 1786.2 260
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0127 -0.2844 -0.2047 -0.0056 5.2673
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 9.917 3.149
## ID.1 Session 3.854 1.963
## Residual 31.422 5.606
## Number of obs: 265, groups: ID, 93
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.0677 0.9668 3.173
## Session 0.9149 0.4753 1.925plot_model(SessionEffectRatio, type = "pred", show.data=TRUE,terms = c("Session"))
# Does session number have an effect on divergent thinking?
ggplot(dataAll, aes(x=as.factor(Session), y=Divergent_Pasta)) +
geom_boxplot(fill="slateblue", alpha=0.2) +
xlab("Session")
SessionEffect2 <- lmer(Divergent_Pasta ~ 1 + Session + (1 + Session|| ID), data = dataAll, REML=F)
print(summary(SessionEffect2), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Divergent_Pasta ~ 1 + Session + ((1 | ID) + (0 + Session | ID))
## Data: dataAll
##
## AIC BIC logLik deviance df.resid
## 1872.3 1890.5 -931.1 1862.3 277
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2533 -0.5594 -0.1156 0.4431 3.5352
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 27.342 5.229
## ID.1 Session 2.999 1.732
## Residual 21.882 4.678
## Number of obs: 282, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 11.1809 0.9133 12.243
## Session -0.5319 0.3851 -1.381plot_model(SessionEffect2, type = "pred", show.data=TRUE, terms = c("Session"))
## look at the correlation between convergent and divergent thinking for each session
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(dataAll[,7:8], pch = 19, cex = 0.5,
col = my_cols[dataAll$Session],
lower.panel=NULL)
# Does drug on its own have an effect on Convergent thinking?
ggplot(dataAll, aes(x=as.factor(Drug), y=Convergent_Pasta)) +
geom_boxplot(fill="slateblue", alpha=0.2) +
xlab("Session")
DrugEffect <- lmer(Convergent_Pasta ~ 1 + Drug + (1 | ID), data = df_MPH, REML=F)
print(summary(DrugEffect), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1523.8 1536.8 -757.9 1515.8 184
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8151 -0.4594 -0.0832 0.3814 3.7880
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 159.59 12.633
## Residual 85.41 9.242
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 22.7447 1.4670 15.504
## Drug1 0.3085 0.6740 0.458plot_model(DrugEffect, type = "pred", show.data=TRUE, terms = c("Drug"))
# Drug effect in the presence of session?
DrugAndSessionEffect <- lmer(Convergent_Pasta ~ 1 + Drug*Session + (1 | ID), data = df_MPH, REML=F)
print(summary(DrugAndSessionEffect), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1520.3 1539.7 -754.1 1508.3 182
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2378 -0.4158 -0.0723 0.3572 4.1477
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 165.74 12.874
## Residual 77.86 8.824
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 18.1707 2.2385 8.117
## Drug1 -1.8587 2.5543 -0.728
## Session 2.3334 0.8697 2.683
## Drug1:Session 1.0552 1.2772 0.826plot_model(DrugAndSessionEffect, type = "pred", show.data=TRUE, terms = c("Drug","Session"))
# Does drug on its own have an effect on Divergent thinking?
ggplot(dataAll, aes(x=as.factor(Drug), y=Divergent_Pasta)) +
geom_boxplot(fill="slateblue", alpha=0.2) +
xlab("Session")
DrugEffect2 <- lmer(Divergent_Pasta ~ 1 + Drug + (1 | ID), data = df_MPH, REML=F)
print(summary(DrugEffect2), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Divergent_Pasta ~ 1 + Drug + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1290.9 1303.8 -641.4 1282.9 184
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3879 -0.4947 -0.0901 0.3593 3.6678
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 37.76 6.145
## Residual 28.00 5.292
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 10.1862 0.7421 13.727
## Drug1 0.6968 0.3859 1.805plot_model(DrugEffect2, type = "pred", terms = c("Drug"))
# Drug effect in the presence of session?
DrugAndSessionEffect2 <- lmer(Divergent_Pasta ~ 1 + Drug* Session + (1 | ID), data = df_MPH, REML=F)
print(summary(DrugAndSessionEffect2), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Divergent_Pasta ~ 1 + Drug * Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1292.0 1311.4 -640.0 1280.0 182
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2919 -0.4825 -0.0912 0.3654 3.7287
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 36.27 6.023
## Residual 27.97 5.289
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 11.1113 1.2360 8.990
## Drug1 2.6937 1.4175 1.900
## Session -0.4498 0.5148 -0.874
## Drug1:Session -1.0190 0.7047 -1.446plot_model(DrugAndSessionEffect2, type = "pred", terms = c("Drug","Session"))
# How do different ROI Ki s relate to each other?
cols <- c(2:4, 7:8)
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(dataAll[,cols], pch = 19, cex = 0.5,
col = my_cols[dataAll$Session],
lower.panel=NULL)
############################# Test the effects of Caudate Ki #############################################################
## effects of MPH vs PBO below
# pure Convergent score
MPH_Caudate_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1522.0 1544.7 -754.0 1508.0 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1830 -0.4290 -0.0762 0.3575 4.1319
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 165.85 12.878
## Residual 77.65 8.812
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 18.7640 10.3977 1.805
## Drug1 -4.0905 4.4653 -0.916
## Caudate_ki -50.2712 722.7969 -0.070
## Session 2.4191 0.8713 2.776
## Drug1:Caudate_ki 305.3485 315.6846 0.967plot_model(MPH_Caudate_PureConvergent, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(MPH_Caudate_PureConvergent) # Plot the model information
qqnorm(residuals(MPH_Caudate_PureConvergent)) 
# Convergent/Divergent ratio score
MPH_Caudate_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1226.7 1248.8 -606.3 1212.7 168
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7803 -0.3750 -0.2319 -0.0082 5.1290
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.16 4.490
## Residual 42.87 6.547
## Number of obs: 175, groups: ID, 91
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.6777 4.8746 0.754
## Drug1 -3.5583 3.3911 -1.049
## Caudate_ki -27.9163 330.1346 -0.085
## Session 0.8616 0.6456 1.335
## Drug1:Caudate_ki 253.1895 239.3929 1.058plot_model(MPH_Caudate_ConDivRatio, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(MPH_Caudate_ConDivRatio)
qqnorm(residuals(MPH_Caudate_ConDivRatio))
# Convergent/Total ratio score
MPH_Caudate_ConTotal <- lmer(Con_Total ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -54.5 -31.9 34.3 -68.5 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0574 -0.5147 0.1010 0.5240 2.1860
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03478 0.1865
## Residual 0.01852 0.1361
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.50870 0.15288 3.327
## Drug1 -0.05650 0.06897 -0.819
## Caudate_ki 6.22253 10.60444 0.587
## Session 0.03407 0.01357 2.511
## Drug1:Caudate_ki 3.18761 4.87762 0.654plot_model(MPH_Caudate_ConTotal, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(MPH_Caudate_ConTotal)
qqnorm(residuals(MPH_Caudate_ConTotal))
# Convergent-Divergent difference score
MPH_Caudate_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1609.7 1632.4 -797.9 1595.7 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1195 -0.4122 -0.0729 0.4347 3.9454
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 232.5 15.25
## Residual 134.8 11.61
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.365 12.624 0.504
## Drug1 -3.333 5.883 -0.566
## Caudate_ki 56.472 874.925 0.065
## Session 2.791 1.142 2.443
## Drug1:Caudate_ki 199.909 415.911 0.481plot_model(MPH_Caudate_ConDifference, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(MPH_Caudate_ConDifference)
qqnorm(residuals(MPH_Caudate_ConDifference))
# Divergent/Total ratio score
MPH_Caudate_DivTotal <- lmer(Div_Total ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -54.4 -31.8 34.2 -68.4 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1847 -0.5250 -0.1048 0.5143 2.0560
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03477 0.1865
## Residual 0.01855 0.1362
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.49187 0.15288 3.217
## Drug1 0.05697 0.06903 0.825
## Caudate_ki -6.22980 10.60419 -0.587
## Session -0.03418 0.01358 -2.517
## Drug1:Caudate_ki -3.21665 4.88194 -0.659plot_model(MPH_Caudate_DivTotal, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(MPH_Caudate_DivTotal)
qqnorm(residuals(MPH_Caudate_DivTotal))
## effects of SUL vs PBO below
# pure Convergent score
SUL_Caudate_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1494.8 1517.5 -740.4 1480.8 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9468 -0.4408 -0.0731 0.2915 3.9815
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 178.63 13.365
## Residual 57.41 7.577
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 18.1860 10.3607 1.755
## Drug1 -4.0681 3.8259 -1.063
## Caudate_ki -88.7444 727.4964 -0.122
## Session 2.7543 0.7906 3.484
## Drug1:Caudate_ki 269.0754 270.9883 0.993plot_model(SUL_Caudate_PureConvergent, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(SUL_Caudate_PureConvergent)
qqnorm(residuals(SUL_Caudate_PureConvergent))
# Convergent/Divergent ratio score
SUL_Caudate_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1206.4 1228.7 -596.2 1192.4 171
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8341 -0.3568 -0.2152 0.0065 5.7767
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 16.10 4.012
## Residual 33.96 5.827
## Number of obs: 178, groups: ID, 93
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 8.3899 4.2775 1.961
## Drug1 0.4849 3.0083 0.161
## Caudate_ki -369.9599 294.5373 -1.256
## Session 0.6971 0.5930 1.175
## Drug1:Caudate_ki -61.0075 213.5831 -0.286plot_model(SUL_Caudate_ConDivRatio, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(SUL_Caudate_ConDivRatio)
qqnorm(residuals(SUL_Caudate_ConDivRatio))
# Convergent/Total ratio score
SUL_Caudate_ConTotal <- lmer(Con_Total ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -50.9 -28.2 32.4 -64.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22401 -0.55586 0.07603 0.51605 1.83188
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03359 0.1833
## Residual 0.01977 0.1406
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.497809 0.150729 3.303
## Drug1 -0.006216 0.071004 -0.088
## Caudate_ki 1.950943 10.536689 0.185
## Session 0.068229 0.014451 4.721
## Drug1:Caudate_ki -0.859790 5.029105 -0.171plot_model(SUL_Caudate_ConTotal, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(SUL_Caudate_ConTotal)
qqnorm(residuals(SUL_Caudate_ConTotal))
# Convergent-Divergent difference score
SUL_Caudate_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1589.6 1612.3 -787.8 1575.6 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7010 -0.4469 -0.0126 0.3783 3.7805
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 217.3 14.74
## Residual 118.1 10.87
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.339 12.009 0.445
## Drug1 -3.695 5.487 -0.673
## Caudate_ki 75.444 840.056 0.090
## Session 3.162 1.119 2.825
## Drug1:Caudate_ki 221.314 388.615 0.569plot_model(SUL_Caudate_ConDifference, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(SUL_Caudate_ConDifference)
qqnorm(residuals(SUL_Caudate_ConDifference))
# Divergent/Total ratio score
SUL_Caudate_DivTotal <- lmer(Div_Total ~ 1 + Drug*Caudate_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * Caudate_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -50.9 -28.3 32.5 -64.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83278 -0.51699 -0.07626 0.55621 2.22345
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03356 0.1832
## Residual 0.01977 0.1406
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.502142 0.150691 3.332
## Drug1 0.006258 0.071005 0.088
## Caudate_ki -1.940537 10.533992 -0.184
## Session -0.068224 0.014451 -4.721
## Drug1:Caudate_ki 0.849165 5.029166 0.169plot_model(SUL_Caudate_DivTotal, type = "pred", terms = c("Caudate_ki","Session","Drug"))
plot(SUL_Caudate_DivTotal)
qqnorm(residuals(SUL_Caudate_DivTotal))
############################# Test the effects of Putamen Ki #############################################################
## effects of MPH vs PBO below
# pure Convergent score
MPH_Putamen_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1522.9 1545.6 -754.5 1508.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1949 -0.4529 -0.0786 0.3663 4.1514
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 165.35 12.859
## Residual 78.46 8.858
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 19.36174 11.43444 1.693
## Drug1 0.04279 5.02649 0.009
## Putamen_ki -69.91372 664.58081 -0.105
## Session 2.35409 0.88805 2.651
## Drug1:Putamen_ki 8.36032 296.07382 0.028plot_model(MPH_Putamen_PureConvergent, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent/Divergent ratio score
MPH_Putamen_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1227.6 1249.8 -606.8 1213.6 168
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8090 -0.3680 -0.2338 -0.0256 5.1362
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.20 4.495
## Residual 43.15 6.569
## Number of obs: 175, groups: ID, 91
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.1489 5.3267 0.779
## Drug1 1.4390 3.7759 0.381
## Putamen_ki -40.7150 303.5188 -0.134
## Session 0.7783 0.6553 1.188
## Drug1:Putamen_ki -86.0849 222.2221 -0.387plot_model(MPH_Putamen_ConDivRatio, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent/Total ratio score
MPH_Putamen_ConTotal <- lmer(Con_Total ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -54.1 -31.5 34.0 -68.1 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0773 -0.4750 0.0937 0.5361 2.1925
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03480 0.1865
## Residual 0.01858 0.1363
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.510966 0.168119 3.039
## Drug1 0.006385 0.077366 0.083
## Putamen_ki 5.199394 9.757426 0.533
## Session 0.032703 0.013777 2.374
## Drug1:Putamen_ki -1.086851 4.556358 -0.239plot_model(MPH_Putamen_ConTotal, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent-Divergent difference score
MPH_Putamen_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1609.9 1632.5 -797.9 1595.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1311 -0.3807 -0.0473 0.4687 3.9547
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 232.2 15.24
## Residual 135.0 11.62
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 9.432 13.874 0.680
## Drug1 1.538 6.593 0.233
## Putamen_ki -121.965 804.469 -0.152
## Session 2.674 1.159 2.306
## Drug1:Putamen_ki -123.085 388.327 -0.317plot_model(MPH_Putamen_ConDifference, type = "pred", terms = c("Putamen_ki","Session","Drug"))
plot(MPH_Putamen_ConDifference)
qqnorm(residuals(MPH_Putamen_ConDifference))
# Divergent/Total ratio score
MPH_Putamen_DivTotal <- lmer(Div_Total ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -53.9 -31.3 34.0 -67.9 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.19125 -0.53605 -0.09337 0.47557 2.07518
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03478 0.1865
## Residual 0.01862 0.1364
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.489172 0.168122 2.910
## Drug1 -0.006116 0.077438 -0.079
## Putamen_ki -5.179289 9.757352 -0.531
## Session -0.032811 0.013789 -2.380
## Drug1:Putamen_ki 1.074502 4.560597 0.236plot_model(MPH_Putamen_DivTotal, type = "pred", terms = c("Putamen_ki","Session","Drug"))
## effects of SUL vs PBO below
# pure Convergent score
SUL_Putamen_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1495.8 1518.4 -740.9 1481.8 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9680 -0.4589 -0.1037 0.3027 4.0126
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 178.33 13.354
## Residual 57.99 7.615
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 17.6047 11.3887 1.546
## Drug1 -1.2096 4.2438 -0.285
## Putamen_ki -44.8624 669.5883 -0.067
## Session 2.8012 0.7939 3.528
## Drug1:Putamen_ki 53.4798 250.1742 0.214plot_model(SUL_Putamen_PureConvergent, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent/Divergent ratio score
SUL_Putamen_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1205.6 1227.9 -595.8 1191.6 171
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8730 -0.3730 -0.2144 0.0592 5.7171
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 17.04 4.128
## Residual 33.11 5.754
## Number of obs: 178, groups: ID, 93
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.1114 4.6993 1.513
## Drug1 4.0429 3.3049 1.223
## Putamen_ki -232.5022 274.6479 -0.847
## Session 0.7103 0.5885 1.207
## Drug1:Putamen_ki -262.3971 194.9995 -1.346plot_model(SUL_Putamen_ConDivRatio, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent/Total ratio score
SUL_Putamen_ConTotal <- lmer(Con_Total ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -51.1 -28.4 32.5 -65.1 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.17868 -0.55058 0.07223 0.50396 1.82385
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03357 0.1832
## Residual 0.01975 0.1405
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.47846 0.16528 2.895
## Drug1 0.01354 0.07831 0.173
## Putamen_ki 2.82104 9.69880 0.291
## Session 0.06784 0.01444 4.699
## Drug1:Putamen_ki -1.88813 4.61658 -0.409plot_model(SUL_Putamen_ConTotal, type = "pred", terms = c("Putamen_ki","Session","Drug"))
# Convergent-Divergent difference score
SUL_Putamen_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1589.9 1612.6 -788.0 1575.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7219 -0.4398 -0.0209 0.3943 3.8002
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 217.2 14.74
## Residual 118.5 10.88
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.5821 13.1780 0.499
## Drug1 -0.7163 6.0655 -0.118
## Putamen_ki -15.8827 773.5282 -0.021
## Session 3.2009 1.1208 2.856
## Drug1:Putamen_ki 6.6629 357.5738 0.019plot_model(SUL_Putamen_ConDifference, type = "pred", terms = c("Putamen_ki","Session","Drug"))
plot(SUL_Putamen_ConDifference)
qqnorm(residuals(SUL_Putamen_ConDifference))
# Divergent/Total ratio score
SUL_Putamen_DivTotal <- lmer(Div_Total ~ 1 + Drug*Putamen_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * Putamen_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -51.1 -28.4 32.6 -65.1 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.82483 -0.50493 -0.07243 0.55023 2.17833
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03355 0.1832
## Residual 0.01975 0.1405
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.52141 0.16524 3.156
## Drug1 -0.01341 0.07831 -0.171
## Putamen_ki -2.80731 9.69635 -0.290
## Session -0.06784 0.01444 -4.698
## Drug1:Putamen_ki 1.87433 4.61667 0.406plot_model(SUL_Putamen_DivTotal, type = "pred", terms = c("Putamen_ki","Session","Drug"))
############################# Test the effects of VS Ki #############################################################
## effects of MPH vs PBO below
# pure Convergent score
MPH_VS_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1520.0 1542.6 -753.0 1506.0 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2164 -0.4128 -0.0839 0.3701 4.1958
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 163.41 12.783
## Residual 77.03 8.777
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.5639 11.5073 0.397
## Drug1 -6.1717 5.0276 -1.228
## VS_ki 907.3052 772.0248 1.175
## Session 2.5662 0.8773 2.925
## Drug1:VS_ki 435.6364 341.8961 1.274plot_model(MPH_VS_PureConvergent, show.values=TRUE, value.offset = .3)
plot_model(MPH_VS_PureConvergent, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent/Divergent ratio score
MPH_VS_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1227.6 1249.7 -606.8 1213.6 168
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7999 -0.3856 -0.2262 -0.0557 5.1621
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 20.07 4.480
## Residual 43.21 6.574
## Number of obs: 175, groups: ID, 91
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.7153 5.4730 0.313
## Drug1 -1.5541 3.8669 -0.402
## VS_ki 108.2781 359.4776 0.301
## Session 0.8644 0.6529 1.324
## Drug1:VS_ki 105.5954 262.7893 0.402plot_model(MPH_VS_ConDivRatio, show.values=TRUE, value.offset = .3)
plot_model(MPH_VS_ConDivRatio, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent/Total ratio score
MPH_VS_ConTotal <- lmer(Con_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -55.1 -32.5 34.5 -69.1 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1040 -0.5145 0.1039 0.5376 2.2060
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03467 0.1862
## Residual 0.01846 0.1359
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.47019 0.17019 2.763
## Drug1 -0.08165 0.07822 -1.044
## VS_ki 8.43062 11.39733 0.740
## Session 0.03556 0.01371 2.593
## Drug1:VS_ki 4.79022 5.32597 0.899plot_model(MPH_VS_ConTotal, show.values=TRUE, value.offset = .3)
plot_model(MPH_VS_ConTotal, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent-Divergent difference score
MPH_VS_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## 1608.0 1630.7 -797.0 1594.0 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1317 -0.3960 -0.0366 0.4012 3.9846
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 228.7 15.12
## Residual 134.2 11.58
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -8.830 13.964 -0.632
## Drug1 -5.837 6.634 -0.880
## VS_ki 1078.450 934.423 1.154
## Session 2.936 1.152 2.549
## Drug1:VS_ki 363.473 451.169 0.806plot_model(MPH_VS_ConDifference, type = "pred", terms = c("VS_ki","Session","Drug"))
plot(MPH_VS_ConDifference)
qqnorm(residuals(MPH_VS_ConDifference))
# Divergent/Total ratio score
MPH_VS_DivTotal <- lmer(Div_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_MPH
##
## AIC BIC logLik deviance df.resid
## -54.9 -32.3 34.5 -68.9 179
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2048 -0.5383 -0.1045 0.5148 2.1020
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03465 0.1861
## Residual 0.01850 0.1360
## Number of obs: 186, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.53043 0.17019 3.117
## Drug1 0.08178 0.07829 1.045
## VS_ki -8.44068 11.39708 -0.741
## Session -0.03566 0.01373 -2.598
## Drug1:VS_ki -4.79473 5.33086 -0.899plot_model(MPH_VS_DivTotal, show.values=TRUE, value.offset = .3)
plot_model(MPH_VS_DivTotal, type = "pred", terms = c("VS_ki","Session","Drug"))
## effects of SUL vs PBO below
# pure Convergent score
SUL_VS_PureConvergent <- lmer(Convergent_Pasta ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_PureConvergent), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Convergent_Pasta ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1493.9 1516.6 -740.0 1479.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9575 -0.4389 -0.0915 0.3183 4.0199
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 176.62 13.290
## Residual 57.41 7.577
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.9742 11.4791 0.520
## Drug1 -4.5189 4.2738 -1.057
## VS_ki 752.8912 779.0124 0.966
## Session 2.7578 0.7898 3.492
## Drug1:VS_ki 289.3428 290.9810 0.994plot_model(SUL_VS_PureConvergent, show.values=TRUE, value.offset = .3)
plot_model(SUL_VS_PureConvergent, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent/Divergent ratio score
SUL_VS_ConDivRatio <- lmer(Con_Div ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_ConDivRatio), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Div ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1207.0 1229.3 -596.5 1193.0 171
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8475 -0.3906 -0.1878 0.0142 5.7288
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 16.72 4.089
## Residual 33.68 5.803
## Number of obs: 178, groups: ID, 93
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.0577 4.8061 1.260
## Drug1 2.4641 3.3765 0.730
## VS_ki -193.9864 322.5798 -0.601
## Session 0.6976 0.5918 1.179
## Drug1:VS_ki -193.7748 229.5201 -0.844plot_model(SUL_VS_ConDivRatio, show.values=TRUE, value.offset = .3)
plot_model(SUL_VS_ConDivRatio, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent/Total ratio score
SUL_VS_ConTotal <- lmer(Con_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_ConTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Con_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -51.3 -28.7 32.7 -65.3 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15283 -0.54150 0.06619 0.52389 1.83639
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03365 0.1834
## Residual 0.01968 0.1403
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.54692 0.16747 3.266
## Drug1 0.03660 0.07912 0.463
## VS_ki -1.51742 11.34328 -0.134
## Session 0.06837 0.01441 4.744
## Drug1:VS_ki -3.76739 5.38705 -0.699plot_model(SUL_VS_ConTotal, show.values=TRUE, value.offset = .3)
plot_model(SUL_VS_ConTotal, type = "pred", terms = c("VS_ki","Session","Drug"))
# Convergent-Divergent difference score
SUL_VS_ConDifference <- lmer(ConDiv_Dif ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_ConDifference), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ConDiv_Dif ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## 1589.1 1611.8 -787.6 1575.1 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7051 -0.4561 -0.0262 0.3903 3.8065
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 214.9 14.66
## Residual 118.4 10.88
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -5.174 13.292 -0.389
## Drug1 -1.919 6.138 -0.313
## VS_ki 796.974 900.504 0.885
## Session 3.143 1.120 2.807
## Drug1:VS_ki 90.778 417.924 0.217plot_model(SUL_VS_ConDifference, type = "pred", terms = c("VS_ki","Session","Drug"))
plot(SUL_VS_ConDifference)
qqnorm(residuals(SUL_VS_ConDifference))
# Divergent/Total ratio score
SUL_VS_DivTotal <- lmer(Div_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_DivTotal), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Div_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
## Data: df_SUL
##
## AIC BIC logLik deviance df.resid
## -51.4 -28.7 32.7 -65.4 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83744 -0.52377 -0.06621 0.54220 2.15188
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.03362 0.1834
## Residual 0.01968 0.1403
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.45326 0.16743 2.707
## Drug1 -0.03678 0.07912 -0.465
## VS_ki 1.51227 11.34036 0.133
## Session -0.06836 0.01441 -4.744
## Drug1:VS_ki 3.77236 5.38706 0.700plot_model(SUL_VS_DivTotal, show.values=TRUE, value.offset = .3)
plot_model(SUL_VS_DivTotal, type = "pred", terms = c("VS_ki","Session","Drug"))
# Factorize the data into score types and test the effects of score type as a factor on scores (1=convergent, 2=divergent)
FactorizedScores <- read.csv('~/Dropbox/DCCN/Creativity/CreativityIDsheet_MissingDataRemoved_ScoresFactorized.csv')
# turn drug conditions into factor levels
FactorizedScores$Drug <- factor(FactorizedScores$Drug, levels = c("MPH","SUL","PBO"));
FactorizedScores$ScoreType <- factor(FactorizedScores$ScoreType);
hs <- function(x) {
y <- 0.5*exp(-x)*(exp(2*x)-1)
return(y)
} # inverse sine transormation
#FactorizedScores$Score <- hs(FactorizedScores$Score); # zscored the scores
FactorizedScores$Score <-scale(FactorizedScores$Score, center = TRUE, scale = TRUE)
# ScoreType 1 refers to convergent, 2 refers to divergent
# set contrasts to sum-to-zero
options(contrasts=c("contr.sum", "contr.poly"))
# set two dataframes for the contrast between MPH and PBO - between SUL and PBO
df_MPH_Factorized <- dplyr::filter(FactorizedScores, Drug %in% c("MPH","PBO"))
df_SUL_Factorized <- dplyr::filter(FactorizedScores, Drug %in% c("SUL","PBO"))## effects of MPH vs PBO on factorized model
MPH_VS_FactorizedScore <- lmer(Score ~ 1 + Drug*VS_ki*ScoreType + Session + (1 | ID), data = df_MPH_Factorized, REML=F)
print(summary(MPH_VS_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * VS_ki * ScoreType + Session + (1 | ID)
## Data: df_MPH_Factorized
##
## AIC BIC logLik deviance df.resid
## 996.0 1039.2 -487.0 974.0 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9628 -0.6380 -0.1269 0.4391 5.9261
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.09562 0.3092
## Residual 0.70010 0.8367
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.46831 0.43371 -1.080
## Drug1 -0.20533 0.33832 -0.607
## VS_ki 25.82118 28.28359 0.913
## ScoreType1 -0.07783 0.33367 -0.233
## Session 0.05047 0.05560 0.908
## Drug1:VS_ki 16.39899 23.01029 0.713
## Drug1:ScoreType1 -0.10384 0.33367 -0.311
## VS_ki:ScoreType1 36.36198 22.72164 1.600
## Drug1:VS_ki:ScoreType1 6.17207 22.72164 0.272plot_model(MPH_VS_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(MPH_VS_FactorizedScore, type = "pred", terms = c("VS_ki","ScoreType","Drug"))
plot(MPH_VS_FactorizedScore)
qqnorm(residuals(MPH_VS_FactorizedScore))
MPH_Putamen_FactorizedScore <- lmer(Score ~ 1 + Drug*Putamen_ki*ScoreType + Session + (1 | ID), data = df_MPH_Factorized, REML=F)
print(summary(MPH_Putamen_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * Putamen_ki * ScoreType + Session + (1 | ID)
## Data: df_MPH_Factorized
##
## AIC BIC logLik deviance df.resid
## 999.5 1042.7 -488.7 977.5 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8945 -0.6345 -0.1318 0.4171 6.0511
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.09574 0.3094
## Residual 0.70728 0.8410
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.05924 0.42951 -0.138
## Drug1 -0.01969 0.33673 -0.058
## Putamen_ki -1.14636 24.26996 -0.047
## ScoreType1 0.55270 0.33113 1.669
## Session 0.04335 0.05601 0.774
## Drug1:Putamen_ki 3.18561 19.83745 0.161
## Drug1:ScoreType1 0.16026 0.33113 0.484
## Putamen_ki:ScoreType1 -6.01111 19.53336 -0.308
## Drug1:Putamen_ki:ScoreType1 -10.36694 19.53336 -0.531plot_model(MPH_Putamen_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(MPH_Putamen_FactorizedScore, type = "pred", terms = c("Putamen_ki","ScoreType","Drug"))
plot(MPH_Putamen_FactorizedScore)
qqnorm(residuals(MPH_Putamen_FactorizedScore))
MPH_Caudate_FactorizedScore <- lmer(Score ~ 1 + Drug*Caudate_ki*ScoreType + Session + (1 | ID), data = df_MPH_Factorized, REML=F)
print(summary(MPH_Caudate_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * Caudate_ki * ScoreType + Session + (1 | ID)
## Data: df_MPH_Factorized
##
## AIC BIC logLik deviance df.resid
## 999.4 1042.6 -488.7 977.4 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9110 -0.6321 -0.1090 0.4160 6.0466
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.09567 0.3093
## Residual 0.70708 0.8409
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.008465 0.392731 0.022
## Drug1 -0.160904 0.301161 -0.534
## Caudate_ki -6.303045 26.389338 -0.239
## ScoreType1 0.454306 0.300111 1.514
## Session 0.043955 0.055316 0.795
## Drug1:Caudate_ki 13.923455 21.293132 0.654
## Drug1:ScoreType1 -0.074264 0.300111 -0.247
## Caudate_ki:ScoreType1 -0.188328 21.233979 -0.009
## Drug1:Caudate_ki:ScoreType1 4.311609 21.233979 0.203plot_model(MPH_Caudate_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(MPH_Caudate_FactorizedScore, type = "pred", terms = c("Caudate_ki","ScoreType","Drug"))
plot(MPH_Caudate_FactorizedScore)
qqnorm(residuals(MPH_Caudate_FactorizedScore))
## effects of SUL vs PBO on factorized model
SUL_VS_FactorizedScore <- lmer(Score ~ 1 + Drug*VS_ki*ScoreType + Session + (1 | ID), data = df_SUL_Factorized, REML=F)
print(summary(SUL_VS_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * VS_ki * ScoreType + Session + (1 | ID)
## Data: df_SUL_Factorized
##
## AIC BIC logLik deviance df.resid
## 972.1 1015.3 -475.1 950.1 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7721 -0.6374 -0.1463 0.4634 4.2719
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.1462 0.3824
## Residual 0.6207 0.7878
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.58791 0.44416 -1.324
## Drug1 -0.25778 0.31422 -0.820
## VS_ki 24.68895 29.91420 0.825
## ScoreType1 -0.03210 0.31418 -0.102
## Session 0.10353 0.05470 1.893
## Drug1:VS_ki 17.51553 21.39430 0.819
## Drug1:ScoreType1 -0.05811 0.31418 -0.185
## VS_ki:ScoreType1 33.65525 21.39408 1.573
## Drug1:VS_ki:ScoreType1 3.46534 21.39408 0.162plot_model(SUL_VS_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(SUL_VS_FactorizedScore, type = "pred", terms = c("VS_ki","ScoreType","Drug"))
plot(SUL_VS_FactorizedScore)
qqnorm(residuals(SUL_VS_FactorizedScore))
SUL_Putamen_FactorizedScore <- lmer(Score ~ 1 + Drug*Putamen_ki*ScoreType + Session + (1 | ID), data = df_SUL_Factorized, REML=F)
print(summary(SUL_Putamen_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * Putamen_ki * ScoreType + Session + (1 | ID)
## Data: df_SUL_Factorized
##
## AIC BIC logLik deviance df.resid
## 975.8 1019.1 -476.9 953.8 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8211 -0.6335 -0.1333 0.4659 4.2397
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.1463 0.3825
## Residual 0.6276 0.7922
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.1788738 0.4399031 -0.407
## Drug1 -0.0659740 0.3121581 -0.211
## Putamen_ki -3.5193419 25.6718981 -0.137
## ScoreType1 0.3914818 0.3119329 1.255
## Session 0.1083277 0.0550036 1.969
## Drug1:Putamen_ki 3.7289515 18.4033478 0.203
## Drug1:ScoreType1 -0.0009515 0.3119329 -0.003
## Putamen_ki:ScoreType1 3.9571244 18.4008205 0.215
## Drug1:Putamen_ki:ScoreType1 -0.3987063 18.4008205 -0.022plot_model(SUL_Putamen_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(SUL_Putamen_FactorizedScore, type = "pred", terms = c("Putamen_ki","ScoreType","Drug"))
plot(SUL_Putamen_FactorizedScore)
qqnorm(residuals(SUL_Putamen_FactorizedScore))
SUL_Caudate_FactorizedScore <- lmer(Score ~ 1 + Drug*Caudate_ki*ScoreType + Session + (1 | ID), data = df_SUL_Factorized, REML=F)
print(summary(SUL_Caudate_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Drug * Caudate_ki * ScoreType + Session + (1 | ID)
## Data: df_SUL_Factorized
##
## AIC BIC logLik deviance df.resid
## 975.2 1018.4 -476.6 953.2 365
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9221 -0.6350 -0.1389 0.4458 4.2110
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.1464 0.3826
## Residual 0.6264 0.7914
## Number of obs: 376, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.09991 0.40323 -0.248
## Drug1 -0.15658 0.28257 -0.554
## Caudate_ki -9.66734 27.83538 -0.347
## ScoreType1 0.37966 0.28246 1.344
## Session 0.10688 0.05489 1.947
## Drug1:Caudate_ki 10.97217 20.01212 0.548
## Drug1:ScoreType1 -0.14891 0.28246 -0.527
## Caudate_ki:ScoreType1 5.60054 19.98541 0.280
## Drug1:Caudate_ki:ScoreType1 10.10048 19.98541 0.505plot_model(SUL_Caudate_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(SUL_Caudate_FactorizedScore, type = "pred", terms = c("Caudate_ki","ScoreType","Drug"))
plot(SUL_Caudate_FactorizedScore)
qqnorm(residuals(SUL_Caudate_FactorizedScore))
## effects of Ki during placebo on factorized model
OnlyPBO <- dplyr::filter(FactorizedScores, Drug %in% c("PBO"))
PBO_VS_FactorizedScore <- lmer(Score ~ 1 + VS_ki*ScoreType + Session + (1 | ID), data = OnlyPBO, REML=F) ## boundary (singular) fit: see ?isSingularprint(summary(PBO_VS_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + VS_ki * ScoreType + Session + (1 | ID)
## Data: OnlyPBO
##
## AIC BIC logLik deviance df.resid
## 503.5 526.2 -244.8 489.5 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7456 -0.6122 -0.1823 0.4128 4.3174
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.0000 0.0000
## Residual 0.7913 0.8896
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.25688 0.51192 -0.502
## VS_ki 9.62638 34.33228 0.280
## ScoreType1 0.02601 0.50169 0.052
## Session 0.04565 0.08047 0.567
## VS_ki:ScoreType1 30.18991 34.16245 0.884
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularplot_model(PBO_VS_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(PBO_VS_FactorizedScore, type = "pred", terms = c("VS_ki","ScoreType"))
plot(PBO_VS_FactorizedScore)
qqnorm(residuals(PBO_VS_FactorizedScore))
PBO_Putamen_FactorizedScore <- lmer(Score ~ 1 + Putamen_ki*ScoreType + Session + (1 | ID), data = OnlyPBO, REML=F) ## boundary (singular) fit: see ?isSingularprint(summary(PBO_Putamen_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Putamen_ki * ScoreType + Session + (1 | ID)
## Data: OnlyPBO
##
## AIC BIC logLik deviance df.resid
## 504.3 527.0 -245.2 490.3 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6556 -0.6098 -0.1839 0.4319 4.2902
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.0000 0.0000
## Residual 0.7947 0.8915
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.04644 0.50472 -0.092
## Putamen_ki -4.60570 29.50633 -0.156
## ScoreType1 0.39243 0.49640 0.791
## Session 0.04945 0.08086 0.612
## Putamen_ki:ScoreType1 4.35583 29.28233 0.149
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularplot_model(PBO_Putamen_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(PBO_Putamen_FactorizedScore, type = "pred", terms = c("Putamen_ki","ScoreType"))
plot(PBO_Putamen_FactorizedScore)
qqnorm(residuals(PBO_Putamen_FactorizedScore))
PBO_Caudate_FactorizedScore <- lmer(Score ~ 1 + Caudate_ki*ScoreType + Session + (1 | ID), data = OnlyPBO, REML=F) ## boundary (singular) fit: see ?isSingularprint(summary(PBO_Caudate_FactorizedScore), corr=F) # print summary without fixed effect correlation matrix## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Score ~ 1 + Caudate_ki * ScoreType + Session + (1 | ID)
## Data: OnlyPBO
##
## AIC BIC logLik deviance df.resid
## 503.9 526.6 -245.0 489.9 181
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6763 -0.6183 -0.2027 0.3962 4.2602
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 0.0000 0.0000
## Residual 0.7931 0.8906
## Number of obs: 188, groups: ID, 94
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.16080 0.47189 0.341
## Caudate_ki -20.25790 31.80862 -0.637
## ScoreType1 0.52857 0.44951 1.176
## Session 0.04874 0.08017 0.608
## Caudate_ki:ScoreType1 -4.49994 31.80427 -0.141
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularplot_model(PBO_Caudate_FactorizedScore, show.values=TRUE, value.offset = .3)
plot_model(PBO_Caudate_FactorizedScore, type = "pred", terms = c("Caudate_ki","ScoreType"))
plot(PBO_Caudate_FactorizedScore)
qqnorm(residuals(PBO_Caudate_FactorizedScore))
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.